C#连接mysql 数据库

【MySQL】C# 连接MySQL 安装完MySQL之后,在安装的默认目录 C:Program Files (x86)MySQLConnector NET 8.0 中查找MySQLData.dll文件。因为作者精力有限,文章中难免出现一些错漏,敬请广大专家和网友批评、指正。在Visual Studio 中为项目中添加引用。 阅读详情

 public class SqlHelpe
    {

        /// <summary>
        /// 建立mysql数据库链接
        /// </summary>
        /// <returns></returns>
        public static MySqlConnection getMySqlCon()
        {
           // string conn = "Data Source=localhost;User ID=root;Password=123;DataBase=googlesearch;Charset=gbk;";
            string conn = "Data Source=192.168.10.80;User ID=admin;Password=admin123;DataBase=googlesearch;Charset=gbk;";
          
            MySqlConnection con = new MySqlConnection(conn);
           
            con.Open();
            return con;
       

          
        }

      


        /// <summary>
        /// 建立执行命令语句对象
        /// </summary>
        /// <param name="sql"></param>
        /// <param name="mysql"></param>
        /// <returns></returns>
        public static MySqlCommand getSqlCommand(String sql, MySqlConnection mysql)
        {
            MySqlCommand cmd = new MySqlCommand(sql, mysql);
            return cmd;
        }

     
        public static int ExecuteNonQuery(string sql, MySqlParameter[] pars)
        {
            MySqlConnection conn = getMySqlCon();
            MySqlCommand comm = new MySqlCommand(sql, conn);
            if (pars != null)
            {
                foreach (MySqlParameter p in pars)
                    comm.Parameters.AddWithValue(p.ParameterName, p.Value);
            }
         
            int i = comm.ExecuteNonQuery();
            conn.Close();
            return i;
        }

        public static object ExecuteScalar(string sql, MySqlParameter[] pars)
        {
            MySqlConnection conn = getMySqlCon();

            MySqlCommand comm = new MySqlCommand(sql, conn);
            if (pars != null)
            {
                foreach (MySqlParameter p in pars)
                    comm.Parameters.AddWithValue(p.ParameterName, p.Value);
            }
           
            object o = comm.ExecuteScalar();
          
            conn.Close();
            return o;
        }


       /// <summary>  
       /// 执行SQL语句,返回影响的记录数  
        /// </summary>  
       /// <param name="sqlString">SQL语句</param>  
        /// <returns>影响的记录数</returns>  
        public static int ExecuteSql(string sql)
        {
            using (MySqlConnection conn = getMySqlCon())
            {
                using (MySqlCommand cmd = new MySqlCommand(sql, conn))
                {
                    try
                    {
                        //conn.Open();
                        int rows = cmd.ExecuteNonQuery();
                        return rows;
                    }
                    catch (System.Data.SqlClient.SqlException E)
                    {
                        conn.Close();
                        throw new Exception(E.Message);
                    }
                    finally
                    {
                        cmd.Dispose(); conn.Close();
                    }
                }
            }
        }

        /// <summary>
        /// 查询并获得结果集并遍历
        /// </summary>
        /// <param name="mySqlCommand"></param>
        public static void getResultset(MySqlCommand mySqlCommand)
        {
            MySqlDataReader reader = mySqlCommand.ExecuteReader();
            try
            {
                while (reader.Read())
                {
                    if (reader.HasRows)
                    {
                        Console.WriteLine("编号:" + reader.GetInt32(0) + "|姓名:" + reader.GetString(1) + "|年龄:" + reader.GetInt32(2) + "|学历:" + reader.GetString(3));
                    }
                }
            }
            catch (Exception)
            {

                Console.WriteLine("查询失败了!");
            }
            finally
            {
                reader.Close();
            }
        }


        /// <summary>
        /// 添加数据
        /// </summary>
        /// <param name="mySqlCommand"></param>
        public static void getInsert(MySqlCommand mySqlCommand)
        {
            try
            {
                mySqlCommand.ExecuteNonQuery();
            }
            catch (Exception ex)
            {
                String message = ex.Message;
                Console.WriteLine("插入数据失败了!" + message);
            }

        }
        /// <summary>
        /// 修改数据
        /// </summary>
        /// <param name="mySqlCommand"></param>
        public static void getUpdate(MySqlCommand mySqlCommand)
        {
            try
            {
                mySqlCommand.ExecuteNonQuery();
            }
            catch (Exception ex)
            {

                String message = ex.Message;
                Console.WriteLine("修改数据失败了!" + message);
            }
        }
        /// <summary>
        /// 删除数据
        /// </summary>
        /// <param name="mySqlCommand"></param>
        public static void getDel(MySqlCommand mySqlCommand)
        {
            try
            {
                mySqlCommand.ExecuteNonQuery();
            }
            catch (Exception ex)
            {
                String message = ex.Message;
                Console.WriteLine("删除数据失败了!" + message);
            }
        }


      


    }

public static object InsertKSOthers(string Kname, string Kvicinity, string Ktype, string Kformatted_phone_number, string Kformatted_address, string Klat, string Klng, string Kurl, string Kicon, string Kreference, string Kid, string Kinternational_phone_number, string Kwebsite)
        {
            string sql = @"insert into KSOthers(Kname,Kvicinity,Ktype,Kformatted_phone_number,Kformatted_address,Klat,Klng,Kurl,Kicon,Kreference,Kid,Kinternational_phone_number,Kwebsite)
                              values(?Kname,?Kvicinity,?Ktype,?Kformatted_phone_number,?Kformatted_address,?Klat,?Klng,?Kurl,?Kicon,?Kreference,?Kid,?Kinternational_phone_number,?Kwebsite)";
            MySqlParameter[] pars = new MySqlParameter[]
           {
               new MySqlParameter("Kname",Kname),
               new MySqlParameter("Kvicinity",Kvicinity),
               new MySqlParameter("Ktype",Ktype),
               new MySqlParameter("Kformatted_phone_number",Kformatted_phone_number),
               new MySqlParameter("Kformatted_address",Kformatted_address),
               new MySqlParameter("Klat",Klat),
               new MySqlParameter("Klng",Klng),
               new MySqlParameter("Kurl",Kurl),
               new MySqlParameter("Kicon",Kicon),
               new MySqlParameter("Kreference",Kreference),
               new MySqlParameter("Kid",Kid),
               new MySqlParameter("Kinternational_phone_number",Kinternational_phone_number),
               new MySqlParameter("Kwebsite",Kwebsite)
            
           };
            //返回受影响行数
            //return DAL.ExecuteNonQuery(sql, pars);
            return SqlHelpe.ExecuteNonQuery(sql, pars);

        }

C#连接mysql数据库实现登陆注册界面【小白教程】 前言: 1.主窗体为登录界面,输入用户名和密码,单击“登录”按钮时从数据库中读取数据,与输入的用户名、密码验证,验证通过后,登录窗体消失。单击“注册”按钮时弹出第三个窗体。 单击“确定”按钮时要把窗体中的输入的数据保存到数据库中 。 环境: Visual Studio 2019 + MySQL8.0 ... 阅读详情

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B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012",}```Contact: rasmusbergpalm at gmail dot comDirectories included in the toolbox-----------------------------------`NN/` - A library for Feedforward Backpropagation Neural Networks`CNN/` - A library for Convolutional Neural Networks`DBN/` - A library for Deep Belief Networks`SAE/` - A library for Stacked Auto-Encoders`CAE/` - A library for Convolutional Auto-Encoders`util/` - Utility functions used by the libraries`data/` - Data used by the examples`tests/` - unit tests to verify toolbox is workingFor references on each library check REFS.mdSetup-----1. Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); 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